Writing reports with R markdown

This report was prepared for the UN . It analyzes the relationship between a countries GDP, life expectancy, and CO2 emissions. Our goal was to determine to what degree a country’s economic strength or weakness may be related to it’s public health status and impact on climate pollution. We hypothesize that both life expectancy and CO2 emissions will increase with a county’s GFP.

n_countries <- gapminder_1997 %>%
  select(country) %>%
  n_distinct()
minGDP <- gapminder_1997 %>%
  summarize(min=min(gdpPercap)) %>%
  round(digits=2)
maxGDP <- gapminder_1997 %>%
  summarize(max=max(gdpPercap)) %>%
  round(digits=2)

The above plot shows the relationship between the GDP per capita and life expectany for a total of 142 countries. For this set of countries, economic wealth ranged from a minimum of $312.19 to a maximum of $4.128316^{4} per capita.

Summary of Data
Number of countries	142
Minimum GDP per cap	312.19
Maximum GDP per cap	4.128316^{4}

gapminder_1997 %>%
  filter(continent == "Oceania") %>%
  kable()

country	pop	continent	lifeExp	gdpPercap
Australia	18565243	Oceania	78.83	26997.94
New Zealand	3676187	Oceania	77.55	21050.41

Using different syntax in RMarkdown

1. R
   • but python is a close second
2. filter()
3. Learn to visualize data more in depth
4. RMarkdown Reference Guide link